Our first comparison is with a modified knowledge distillation [1]} method, where we adapt knowledge distillation to a real-time, actively learning scenario.
Since pairwise knowledge transfer methods such as transfer learning and knowledge distillation are not designed for real-time knowledge transfer, we compare with a variation of these that is a valid option for real-time execution. The basic form of knowledge distillation can be applied to a real-time knowledge transfer case by minimizing the logit distance of the source and target models as they are learning new data.
Our second comparison is based on [2]}, where we implement an ADMM [3]} Gossip algorithms based neighbor-to-neighbor knowledge transfer with a random component. Lastly, we compare to a decentralized, federated learning approach for non-i.i.d. data as described in [4]}, [5]}. We consider the consolidated result of this approach to be comparable to our local model when only a single agent's improvement is measured, where that agent corresponds to our termed local agent.
